41 citations · 152 across the 18 of their papers we have counts for
21 papers
Training a Predictive Coding Network on ImageNet using Equilibrium Propagation
Tugdual Kerjan, Rasmus Høier, Benjamin Scellier
Equilibrium Propagation (EP) is a physics-based training framework that has primarily been employed in energy-based models, including continuous Hopfield networks, nonlinear resist…
Training of Physical Neural Networks
Ali Momeni, Babak Rahmani, Benjamin Scellier +25
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research…
Quantum Equilibrium Propagation: Gradient-Descent Training of Quantum Systems
Benjamin Scellier
Equilibrium propagation (EP) is a training framework for energy-based systems, i.e. systems whose physics minimizes an energy function. EP has been explored in various classical ph…
A Fast Algorithm to Simulate Nonlinear Resistive Networks
Benjamin Scellier
Analog electrical networks have long been investigated as energy-efficient computing platforms for machine learning, leveraging analog physics during inference. More recently, resi…
Energy-based learning algorithms for analog computing: a comparative study
Benjamin Scellier, Maxence Ernoult, Jack Kendall +1
Energy-based learning algorithms have recently gained a surge of interest due to their compatibility with analog (post-digital) hardware. Existing algorithms include contrastive le…
Temporal Contrastive Learning through implicit non-equilibrium memory
Martin J. Falk, Adam T. Strupp, Benjamin Scellier +1
The backpropagation method has enabled transformative uses of neural networks. Alternatively, for energy-based models, local learning methods involving only nearby neurons offer be…